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Target Volume Delineation in Dynamic Positron Emission Tomography Based on Time Activity Curve Differences Open Access


Other title
Positron Emission Tomography
Image segmentation
Target Volume Delineation
Time Activity Curve
Radiation treatment planning
Type of item
Degree grantor
University of Alberta
Author or creator
Teymurazyan, Artur
Supervisor and department
Robinson, Don (Department of Oncology)
Riauka, Terry (Department of Oncology)
Examining committee member and department
Sloboda, Ron (Department of Oncology)
Stodilka, Robert (Medical Imaging, Western University)
Jans, Hans-Sönke (Department of Oncology)
Marchand, Richard (Department of Physics)
Department of Physics
Medical Physics
Date accepted
Graduation date
Doctor of Philosophy
Degree level
Tumor volume delineation plays a critical role in radiation treatment planning and simulation, since inaccurately defined treatment volumes may lead to the overdosing of normal surrounding structures and potentially missing the cancerous tissue. However, the imaging modality almost exclusively used to determine tumor volumes, X-ray Computed Tomography (CT), does not readily exhibit a distinction between cancerous and normal tissue. It has been shown that CT data augmented with PET can improve radiation treatment plans by providing functional information not available otherwise. Presently, static PET scans account for the majority of procedures performed in clinical practice. In the radiation therapy (RT) setting, these scans are visually inspected by a radiation oncologist for the purpose of tumor volume delineation. This approach, however, often results in significant interobserver variability when comparing contours drawn by different experts on the same PET/CT data sets. For this reason, a search for more objective contouring approaches is underway. The major drawback of conventional tumor delineation in static PET images is the fact that two neighboring voxels of the same intensity can exhibit markedly different overall dynamics. Therefore, equal intensity voxels in a static analysis of a PET image may be falsely classified as belonging to the same tissue. Dynamic PET allows the evaluation of image data in the temporal domain, which often describes specific biochemical properties of the imaged tissues. Analysis of dynamic PET data can be used to improve classification of the imaged volume into cancerous and normal tissue. In this thesis we present a novel tumor volume delineation approach (Single Seed Region Growing algorithm in 4D (dynamic) PET or SSRG/4D-PET) in dynamic PET based on TAC (Time Activity Curve) differences. A partially-supervised approach is pursued in order to allow an expert reader to utilize the information available from other imaging modalities routinely used in conjunction with PET. In our scheme, this includes the definition of a tumor encompassing mask and selection of a seed site within the suspected tumor, while further delineation is performed automatically by the algorithm. The development of this method is examined and improved classification of the imaged volume into cancerous and normal tissue compared to methods currently used in the clinic is demonstrated.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
“Single Seed Region Growing Algorithm in Dynamic PET imaging (SSRG/4D-PET) for Tumor Volume Delineation in Radiotherapy Treatment Planning: Theory and Simulation”, A. Teymurazyan, R. Sloboda, T. Riauka, H-S. Jans, and D. Robinson, IEEE T. Nucl. Sci. Vol. 59, 5(1) (DOI: 10.1109/TNS.2012.2212723), 2020, (2012)“Properties of noise in positron emission tomography images reconstructed with filtered-back-projection and row-action maximum likelihood algorithm”, A. Teymurazyan, T. Riauka, H-S. Jans, and D. Robinson, J. Digit. Imaging. [Epub ahead of print] (DOI: 10.1007/s10278-012-9511-5, Accepted 04-Aug-2012), 1-10, (2012)

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